A deep learning approach for face recognition based on angularly discriminative features

Iqbal, Mansoor; Sameem, M. Shujah Islam; Naqvi, Nuzhat; Kanwal, Shamsa; Ye, Zhongfu

doi:10.1016/j.patrec.2019.10.002

Cited by 48 publications

(12 citation statements)

References 15 publications

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“…They robustly extracted the features by deep properties with a cross-resolution scenario. In [26], the angularly discriminative features based on deep learning for face recognition are utilized.…”

Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

Ahmed

Frikha

Hussein

et al. 2021

BioMed Research International

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In this study, Gabor wavelet transform on the strength of deep learning which is a new approach for the symmetry face database is presented. A proposed face recognition system was developed to be used for different purposes. We used Gabor wavelet transform for feature extraction of symmetry face training data, and then, we used the deep learning method for recognition. We implemented and evaluated the proposed method on ORL and YALE databases with MATLAB 2020a. Moreover, the same experiments were conducted applying particle swarm optimization (PSO) for the feature selection approach. The implementation of Gabor wavelet feature extraction with a high number of training image samples has proved to be more effective than other methods in our study. The recognition rate when implementing the PSO methods on the ORL database is 85.42% while it is 92% with the three methods on the YALE database. However, the use of the PSO algorithm has increased the accuracy rate to 96.22% for the ORL database and 94.66% for the YALE database.

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Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

Ahmed

Frikha

Hussein

et al. 2021

BioMed Research International

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“…Iqbal et al [18] examined hybrid angularly discriminative features by amalgamating multiplicative angular together with an additive cosine margin for enhancing the efcacy of angular SoftMax loss as well as large margin cosine. By utilizing the CASIA-WebFace dataset, the model was trained; subsequently, on YouTube Faces (YTF), Labeled Face in the Wild (LFW), VGGFace1, and VGGFace2, the testing was conducted.…”

Section: Literature Surveymentioning

confidence: 99%

An Efficient Face Detection and Recognition System Using RVJA and SCNN

Janarthanan

Murugesh

Sivakumar

et al. 2022

Mathematical Problems in Engineering

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The basic process for an extensive range of security systems functioning in real-time applications is facial recognition. Considering several factors like lower resolution, occlusion, illumination, noise, along with pose variation, a satisfactory outcome was not achieved by various models developed for face recognition (FR). Therefore, by utilizing reconstruction scheme-centric Viola–Jones algorithm (RVJA) and shallowest sketch-centered convolution neural network (SCNN) methodologies, an effectual face detection and recognition (FDR) system has been proposed here by considering the aforementioned factors. Specifically, first, the algorithm identifies faces in a provided image by determining its global facial model in various positions along with poses; then, it sequentially enhanced the recognition outcome by utilizing SCNN. Initially, by employing the RVJA, face detection (FD) has been performed. The unconstrained face images are handled by the proposed RVJA having efficient properties such as boundedness and invariance, together with the ability to rebuild the actual image. After that, for FR, the SCNN methodology is utilized, thus learning the complicated features of the face-detected images. Next, regarding metrics like area under curve (AUC), recognition accuracy (RA), and average precision (AP), the proposed methodology’s experiential outcome is analogized with other prevailing methodologies. The experimental outcome displayed that the facial images are recognized by the proposed model with higher accuracy than that of the other conventional methodologies.

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“…So, it is necessary to effectively extract the features of input samples based on a certain algorithm, and then classify the input samples by training the classifier. Pattern classification is widely used in modern society, especially in the fields of face recognition [1], [2], object detection [3], [4], object segmentation [5], [6], text classification [7], [8] and so on. It is an important foundation of artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Generation and Frame Characteristics of Predefined Evenly-Distributed Class Centroids for Pattern Classification

et al. 2021

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Predefined evenly-distributed class centroids (PEDCC) can be widely used in models and algorithms of pattern classification, such as CNN classifiers, classification autoencoders, clustering, and semi-supervised learning, etc. Its basic idea is to predefine the class centers, which are evenly-distributed on the unit hypersphere in feature space, to maximize the inter-class distance. The previous method of generating PEDCC uses an iterative algorithm based on a charge model. The generated class centers will have some errors with the theoretically evenly-distributed points, and the generation time is long. This paper takes advantage of regular polyhedron in high-dimensional space and the evenly distributed points on the n dimensional hypersphere to generate PEDCC mathematically. Then, we discussed the basic and extended characteristics of the frames formed by PEDCC, and some meaningful conclusions are obtained. Finally, the effectiveness of the new algorithm and related conclusions are proved by experiments. The mathematical analysis and experimental results of this paper can provide a theoretical tool for using PEDCC to solve the key problems in the field of pattern recognition, such as interpretable supervised/unsupervised learning, incremental learning, uncertainty analysis and so on.INDEX TERMS Predefined evenly-distributed class centroids, polyhedron in high-dimensional space, pattern classification, CNN classifiers, frame theory.

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A deep learning approach for face recognition based on angularly discriminative features

Cited by 48 publications

References 15 publications

[Retracted] Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

[Retracted] Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

An Efficient Face Detection and Recognition System Using RVJA and SCNN

Generation and Frame Characteristics of Predefined Evenly-Distributed Class Centroids for Pattern Classification

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